Fix issue with missing instances and categorical entity index

[thehomebrewnerd]

Fixes #1046

Changed order of operations in merging default dataframe into feature matrix to allow combination of categorical and integer indexes. Added test case to cover this situation.

[codecov]

Codecov Report

Merging #1050 into main will not change coverage.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##             main    #1050   +/-   ##
=======================================
  Coverage   98.35%   98.35%           
=======================================
  Files         126      126           
  Lines       13082    13082           
=======================================
  Hits        12867    12867           
  Misses        215      215           

Impacted Files	Coverage Δ
...computational_backends/calculate_feature_matrix.py	99.07% <100.00%> (+<0.01%)	⬆️
...s/computational_backends/feature_set_calculator.py	98.68% <100.00%> (+<0.01%)	⬆️
...utational_backend/test_calculate_feature_matrix.py	98.20% <100.00%> (+0.01%)	⬆️
...mputational_backend/test_feature_set_calculator.py	97.93% <100.00%> (-0.03%)	⬇️

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[thehomebrewnerd]

@rwedge Added checks to make sure that the feature matrix has a categorical index, if the target entity has a categorical index. If the user supplies an instance id that is not in the target entity, the index categories will be different in the feature matrix (to account for adding the missing ids), but the index dtype will be maintained.

This change required some updates to a few tests which had errors due to an invalid indexing approach being used for a series with a categorical index. For example, df[col][0] returns the first value in the series if the index is not categorical, but results in an error if the index is categorical. Changed to df[col].values[0] which works for both.

[rwedge]

Looks good